import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


def 				lowerCAmelCase__       (			a__ )      ->str:




  '''simple docstring'''

  _UpperCamelCase           =    filter(lambda a__ : p.requires_grad    ,     model.parameters() )
  _UpperCamelCase           =    sum([np.prod(p.size() ) for p in model_parameters] )
  return params


lowerCamelCase__  =					logging.getLogger(__name__)


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Dict:




  '''simple docstring'''

  if metric == "rouge2":
    _UpperCamelCase           =    "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    _UpperCamelCase           =    "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "em":
    _UpperCamelCase           =    "{val_avg_em:.4f}-{step_count}"
  else:
    raise NotImplementedError(
        f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
        " function." )

  _UpperCamelCase           =    ModelCheckpoint(
      dirpath=a__    ,     filename=a__    ,     monitor=f'val_{metric}'    ,     mode="max"    ,     save_top_k=3    ,     every_n_epochs=1    ,     )
  return checkpoint_callback


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Optional[Any]:




  '''simple docstring'''

  return EarlyStopping(
      monitor=f'val_{metric}'    ,     mode="min" if "loss" in metric else "max"    ,     patience=a__    ,     verbose=a__    ,     )







class 					_UpperCAmelCase			(     pl.Callback  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    {f'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
   pl_module.logger.log_metrics(lowercase_)







 @rank_zero_only
 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   pl.Trainer							, lowercase_				:   pl.LightningModule							, lowercase_				:   str							, lowercase_				:   Any=True)						->	None:


   """simple docstring"""







   logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****')
   _UpperCamelCase           =    trainer.callback_metrics
   trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]})
   # Log results
   _UpperCamelCase           =    Path(pl_module.hparams.output_dir)
   if type_path == "test":
     _UpperCamelCase           =    od / "test_results.txt"
     _UpperCamelCase           =    od / "test_generations.txt"
   else:
     # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
     # If people want this it will be easy enough to add back.
     _UpperCamelCase           =    od / f'{type_path}_results/{trainer.global_step:05d}.txt'
     _UpperCamelCase           =    od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
     results_file.parent.mkdir(exist_ok=lowercase_)
     generations_file.parent.mkdir(exist_ok=lowercase_)
   with open(lowercase_							, "a+") as writer:
     for key in sorted(lowercase_):
       if key in ["log", "progress_bar", "preds"]:
         continue
       _UpperCamelCase           =    metrics[key]
       if isinstance(lowercase_							, torch.Tensor):
         _UpperCamelCase           =    val.item()
       _UpperCamelCase           =    f'{key}: {val:.6f}\n'
       writer.write(lowercase_)

   if not save_generations:
     return

   if "preds" in metrics:
     _UpperCamelCase           =    "\n".join(metrics["preds"])
     generations_file.open("w+").write(lowercase_)







 @rank_zero_only
 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	Optional[Any]:


   """simple docstring"""







   try:
     _UpperCamelCase           =    pl_module.model.model.num_parameters()
   except AttributeError:
     _UpperCamelCase           =    pl_module.model.num_parameters()

   _UpperCamelCase           =    count_trainable_parameters(lowercase_)
   # mp stands for million parameters
   trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6})







 @rank_zero_only
 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   pl.Trainer							, lowercase_				:   pl.LightningModule)						->	Union[str, Any]:


   """simple docstring"""







   save_json(pl_module.metrics							, pl_module.metrics_save_path)
   return self._write_logs(lowercase_							, lowercase_							, "test")







 @rank_zero_only
 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   pl.Trainer							, lowercase_				:   Optional[int])						->	Optional[int]:


   """simple docstring"""







   save_json(pl_module.metrics							, pl_module.metrics_save_path)
   # Uncommenting this will save val generations
   # return self._write_logs(trainer, pl_module, "valid")





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




